Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next sampling...
Basic Continuous Time Signals01:22

Basic Continuous Time Signals

Basic continuous-time signals include the unit step function, unit impulse function, and unit ramp function, collectively referred to as singularity functions. Singularity functions are characterized by discontinuities or discontinuous derivatives.
The unit step function, denoted u(t), is zero for negative time values and one for positive time values, exhibiting a discontinuity at t=0. This function often represents abrupt changes, such as the step voltage introduced when turning a car's...
Assessing Body Temperature - Temporal Artery01:19

Assessing Body Temperature - Temporal Artery

Here is a stepwise guide to assessing the body temperature at the temporal artery using a temporal artery thermometer
Step 1: Perform hand hygiene and don a fresh pair of gloves to prevent cross-infection and ensure patient safety.
Step 2: Explain the procedure to the patient to establish trust. Clear communication establishes trust with the patient, ensures they understand what to expect, promotes cooperation, and enhances comfort during the procedure.  
Step 3: Assess the patient's forehead...
Discrete-time Fourier transform01:26

Discrete-time Fourier transform

The Discrete-Time Fourier Transform (DTFT) is an essential mathematical tool for analyzing discrete-time signals, converting them from the time domain to the frequency domain. This transformation allows for examining the frequency components of discrete signals, providing insights into their spectral characteristics. In the DTFT, the continuous integral used in the continuous-time Fourier transform is replaced by a summation to accommodate the discrete nature of the signal.
One of the notable...
Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Attention and learning strategies reveal distinct dimensions of psychiatric diseases.

Research square·2026
Same author

Point2SSM++: Self-supervised learning of anatomical shape models from point clouds.

Medical image analysis·2026
Same author

A Pax7::Foxo1 conditional mouse strain.

Skeletal muscle·2026
Same author

Comparison of LGE MRI Scar Identification Methods for Atrial Computational Modeling.

Computing in cardiology·2026
Same author

HAMIL-QA: Hierarchical Approach to Multiple Instance Learning for Atrial LGE MRI Quality Assessment.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2025
Same author

Quantifying Sagittal Craniosynostosis Severity: A Machine Learning Approach With CranioRate.

The Cleft palate-craniofacial journal : official publication of the American Cleft Palate-Craniofacial Association·2025

Related Experiment Video

Updated: Jul 12, 2026

Bringing the Clinic Home: An At-Home Multi-Modal Data Collection Ecosystem to Support Adaptive Deep Brain Stimulation
06:32

Bringing the Clinic Home: An At-Home Multi-Modal Data Collection Ecosystem to Support Adaptive Deep Brain Stimulation

Published on: July 14, 2023

Timesynth: A Temporal Fidelity Framework for Health Signal Digital Twins.

Warren Pettine, Md Rakibul Haque, Shireen Elhabian

    Research Square
    |July 10, 2026
    PubMed
    Summary

    Standard metrics fail to evaluate health-signal forecasting models, misranking them and hiding critical phase accuracy loss. TimeSynth, a new framework, benchmarks model fidelity to physiological dynamics, enabling better digital twin development.

    More Related Videos

    Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
    10:28

    Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

    Published on: July 24, 2019

    Related Experiment Videos

    Last Updated: Jul 12, 2026

    Bringing the Clinic Home: An At-Home Multi-Modal Data Collection Ecosystem to Support Adaptive Deep Brain Stimulation
    06:32

    Bringing the Clinic Home: An At-Home Multi-Modal Data Collection Ecosystem to Support Adaptive Deep Brain Stimulation

    Published on: July 14, 2023

    Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
    10:28

    Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

    Published on: July 24, 2019

    Area of Science:

    • * Computational Biology
    • * Biomedical Engineering
    • * Signal Processing

    Background:

    • * Health-signal digital twins require models preserving physiological dynamics (oscillation, frequency, phase, state-transition).
    • * Current pointwise metrics fail to detect loss of these fundamental dynamics, leading to inaccurate model benchmarking.
    • * This limitation results in models with similar pointwise error exhibiting significant phase accuracy divergence, undetectable by standard evaluation.

    Purpose of the Study:

    • * To introduce TimeSynth, a controlled benchmarking framework for evaluating health-signal forecasting models.
    • * To enable the development of models that accurately preserve physiological signal dynamics.
    • * To provide a preclinical stress test for models before integration with patient data.

    Main Methods:

    • * Development of TimeSynth, a framework with a physiologically grounded signal generator and fidelity diagnostics.
    • * Generation of synthetic signals with analytically known ground-truth dynamics from parametric models fitted to real electroencephalography (EEG), electrocardiography (ECG), and photoplethysmogram (PPG) data.
    • * Quantification of amplitude, frequency, phase, and state-transition fidelity using TimeSynth diagnostics.

    Main Results:

    • * Standard metrics misrank models, with up to 53-degree phase accuracy divergence (approx. 123 ms at 1.2 Hz) missed.
    • * Linear and full-sequence attention models lose frequency and phase information despite acceptable amplitude error.
    • * Architectures with localized temporal structure better preserve dynamical fidelity and state transitions, but none reliably preserve stochastic switching.

    Conclusions:

    • * Model architecture is the primary determinant of fidelity, making model choice a principled, use-case-driven decision.
    • * TimeSynth provides essential diagnostics for fidelity-aware development of health-signal forecasting models.
    • * The framework facilitates controlled preclinical stress testing, crucial before coupling models to patient data.